Embark on an enlightening exploration of stroke prediction with this compelling data analysis project presented by Boston Institute of Analytics. Our dedicated students delve into the intricate world of healthcare analytics, employing advanced data analysis techniques to forecast and identify potential stroke risks. From analyzing medical records and demographic factors to evaluating lifestyle habits and genetic predispositions, this project offers a comprehensive examination of the variables influencing stroke occurrences. Gain invaluable insights and actionable recommendations derived from rigorous data analysis, presented in an engaging and informative format. Don't miss this opportunity to delve into the fascinating realm of healthcare analytics and uncover new perspectives on stroke prediction. Explore the project now and embark on a journey of discovery with Boston Institute of Analytics. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.

2. Topic : Stroke Prediction
Presented by Mansi Choudhary

3. Content
• Introduction and Problem Statement
• Importing libraries
• Data Understanding
• Data Cleaning
• Data Visualization
• Data Preprocessing
• Model Selection
• Model Comparison
• Testing
• Conclusion

4. Introduction
According to the World Health Organization (WHO) stroke is
the 2nd leading cause of death globally, responsible for
approximately 11% of total deaths.
This dataset is used to predict whether a patient is likely to get
stroke based on the input parameters like gender, age, various
diseases, and smoking status. Each row in the data provides
relevant information about the patient

5. Problem Statement
The goal of this exploratory analysis is to develop an effective
model for predicting the likelihood of strokes in individuals
within a healthcare dataset

6. Importing libraries

7. Data Understanding
• Displaying Dataset
• Data shape

8. Data Understanding
• Description of Numerical data
• Description of Categorical data

9. Data Cleaning
• Checking null values
After imputation null values
Checking Duplicate Values

10. Data Cleaning
• Dropped ‘id’ column
• Checking spelling mistake and unique values

11. Data Cleaning
Checking Outliers

12. Data Visualization
• Analysis for Categorical data

13. Data Visualization
• Analysis for Binary number data

14. Data Visualization
• Data distribution nature

15. Data Visualization
• Hypothesis testing for BMI , Average Glucose
level and age

16. Data Visualization
• Age appears to have a notable influence

17. Data Visualization
Aged between 20 to 60
years often exhibit
higher BMI values
Significantly heart
Diseases increases above
the age of 30

18. Data Visualization
Significantly hypertension
increase above the age of
20

19. Data Visualization
Patients above age of 40
shows higher average
glucose level
Stroke rate illustrate a
notable rise after the age
of 40

20. Data Visualization
Stroke patients had either
low or high glucose
levels
BMI between 40 and 60
exhibit high average glucose
levels, while those with a BMI
exceeding 60 show low
average glucose levels.

21. Data Visualization
Despite the absence of heart disease or hypertension,
data reveals that some patients still experienced strokes

22. Data Visualization
Formerly smoking individuals have a higher average
glucose level.

23. Data Visualization
Private sector tend to have
higher average glucose
levels
Private-sector jobs show the
highest stroke incidence

24. Data Visualization
Married individuals, reporting a higher average glucose level, also
experience the highest incidence of strokes

25. Data Visualization
Urban individuals with slightly higher average glucose levels
also experience a slightly elevated stroke incidence compared to
rural counterparts

26. Data Visualization
• Binning BMI column
BMI Weight Status
Below 18.5 Underweight
18.5—24.9 Healthy Weight
25.0—29.9 Overweight
30.0 and Above Obesity

27. Describing columns based on stroke occurrence

28. Data preprocessing
Used Label
Encoder
Splitting up
the data
SMOTE
Feature Scaling

29. Data preprocessing
• Applied Label Encoder
• Data Splitting

30. Data preprocessing
• Feature Scaling
• SMOTE

31. Data preprocessing
• SMOTE Visualization

32. Identifying the best feature
The feature 'age' has the greatest influence among all
features

33. Model Selection
Models
K Neighbors
Classifier
Decision Tree
Classifier
Gaussian
NAIVE BAYES
Random
Forest
Classifier
Support Vector
Classifier
Logistic
Regression

34. Model Performance
Best Model : Gaussian Naive Bayes

35. Testing

36. Conclusion
As age increases,
the likelihood of
experiencing a
stroke also
increases
Most people
experience a
stroke when they
have either a low
or high average
glucose level
Private sector
has shown a
higher number
of stroke cases
Even when patients
don't have
hypertension or
heart disease, they
still experience
strokes
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37. Thank You!!